mecfs_bio.asset_generator.concrete_sldsc_generator

Asset generator for applying S-LDSC using standard reference datasets.

Functions:

• standard_sldsc_task_generator –

  Standardized task generator for applying S-LSDC to GWAS summary statistics

Attributes:

• IMMGEN_LABELS –
• MULTI_TISSUE_CHROMATIN_LABELS –
• MULTI_TISSUE_GENE_EXPR_LABELS –

IMMGEN_LABELS module-attribute

IMMGEN_LABELS = CellOrTissueLabelRecord(
    FICUANE_2018_IMMGEN_CATEGORIES,
    pipe_left=CompositePipe(
        [
            StrLowercasePipe(
                target_column="Name",
                new_column_name="lower case name",
            )
        ]
    ),
    pipe_right=CompositePipe(
        [
            StrLowercasePipe(
                target_column="Cell Type",
                new_column_name="lower case name",
            )
        ]
    ),
    left_join_on="lower case name",
    right_join_on="lower case name",
)

MULTI_TISSUE_CHROMATIN_LABELS module-attribute

MULTI_TISSUE_CHROMATIN_LABELS = CellOrTissueLabelRecord(
    ROADMAP_CELL_TYPE_CATEGORIES_FOR_LDSC,
    pipe_left=CompositePipe(
        [
            SplitExactColPipe(
                col_to_split="Name",
                split_by="__",
                new_col_names=("Cell", "Epigenetic_Assay"),
            ),
            StrReplacePipe(
                target_column="Cell",
                new_column_name="Cell",
                replace_what="_ENTEX",
                replace_with="",
            ),
            StrLowercasePipe(
                target_column="Cell", new_column_name="Cell"
            ),
        ]
    ),
    pipe_right=CompositePipe(
        [
            StrLowercasePipe(
                target_column="Cell Type",
                new_column_name="Cell type",
            )
        ]
    ),
    left_join_on="Cell",
    right_join_on="Cell type",
)

MULTI_TISSUE_GENE_EXPR_LABELS module-attribute

MULTI_TISSUE_GENE_EXPR_LABELS = CellOrTissueLabelRecord(
    FICUANE_2018_FRANKE_GTEX_CATEGORIES,
    pipe_left=CompositePipe(
        [
            StrLowercasePipe(
                target_column="Name", new_column_name="Name"
            ),
            StrReplacePipe(
                target_column="Name",
                new_column_name="Name",
                replace_what=" ",
                replace_with="_",
            ),
        ]
    ),
    pipe_right=CompositePipe(
        [
            StrLowercasePipe(
                target_column="Tissue_Or_Cell",
                new_column_name="Tissue_Or_Cell",
            ),
            StrReplacePipe(
                target_column="Tissue_Or_Cell",
                new_column_name="Tissue_Or_Cell",
                replace_what=" ",
                replace_with="_",
            ),
        ]
    ),
)

standard_sldsc_task_generator

standard_sldsc_task_generator(
    sumstats_task: Task,
    base_name: str,
    pre_pipe: DataProcessingPipe = IdentityPipe(),
) -> SLDSCTaskGenerator

Standardized task generator for applying S-LSDC to GWAS summary statistics

as described in "Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types." Nature genetics 50.4 (2018): 621-629.

using the standard partitioning datasets provided by the authors of that paper

Source code in mecfs_bio/asset_generator/concrete_sldsc_generator.py

def standard_sldsc_task_generator(
    sumstats_task: Task,
    base_name: str,
    pre_pipe: DataProcessingPipe = IdentityPipe(),
) -> SLDSCTaskGenerator:
    """
    Standardized task generator for applying S-LSDC to GWAS summary statistics

    as described in
    "Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types." Nature genetics 50.4 (2018): 621-629.

    using the standard partitioning datasets provided by the authors of that paper

    """
    result = SLDSCTaskGenerator.create(
        base_name=base_name,
        source_sumstats_task=sumstats_task,
        ref_ld_chr_task=BASE_MODEL_PARTITIONED_LD_SCORES_EXTRACTED,
        ref_ld_chr_inner_dirname="baseline_v1.2/baseline.@",
        w_ld_chr_task=PARTITIONED_MODEL_REGRESSION_WEIGHTS_EXTRACTED,
        w_ld_chr_inner_dirname="1000G_Phase3_weights_hm3_no_MHC/weights.hm3_noMHC.@",
        partitioned_entries=[
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_CAHOY_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="Cahoy.ldcts",
                cell_or_tissue_labels_task=None,
                entry_name="cahoy_cns",
            ),
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_GTEX_BRAIN_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="GTEx_brain.ldcts",
                cell_or_tissue_labels_task=None,
                entry_name="gtex_brain",
            ),
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_IMMGEN_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="ImmGen.ldcts",
                cell_or_tissue_labels_task=IMMGEN_LABELS,
                entry_name="immgen",
            ),
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_CORCES_ATAC_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="Corces_ATAC.ldcts",
                cell_or_tissue_labels_task=None,
                entry_name="corces_atac",
            ),
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_MULTI_TISSUE_CHROMATIN_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="Multi_tissue_chromatin.ldcts",
                entry_name="multi_tissue_chromatin",
                cell_or_tissue_labels_task=MULTI_TISSUE_CHROMATIN_LABELS,
            ),
            PartitionedLDScoresRecord(
                ref_ld_chr_cts_task=PARTITIONED_MODEL_MULTI_TISSUE_GENE_EXPR_LD_SCORES_EXTRACTED_RD,
                ref_ld_chr_cts_filename="Multi_tissue_gene_expr.ldcts",
                cell_or_tissue_labels_task=MULTI_TISSUE_GENE_EXPR_LABELS,
                entry_name="multi_tissue_gene_expression",
            ),
        ],
        multiple_testing_alpha=0.01,
        multiple_testing_method="fdr_bh",
        pre_pipe=pre_pipe,
    )
    return result